code: def generate(in_seq, enc_fwd_lstm, enc_bwd_lstm, dec_lstm):
      embedded = embed_sentence(in_seq)
      encoded = encode_sentence(enc_fwd_lstm, enc_bwd_lstm, embedded)
  
      w = dy.parameter(decoder_w)
      b = dy.parameter(decoder_b)
      w1 = dy.parameter(attention_w1)
      input_mat = dy.concatenate_cols(encoded)
      w1dt = None
  
      last_output_embeddings = output_lookup[char2int[EOS]]
      s = dec_lstm.initial_state().add_input(
          dy.concatenate(
              [dy.vecInput(STATE_SIZE * 2), last_output_embeddings]
          )
      )
  
      out = ""
      count_EOS = 0
      for i in range(len(in_seq) * 2):
          if count_EOS == 2:
              break
          # w1dt can be computed and cached once for the entire decoding phase
          w1dt = w1dt or w1 * input_mat
          vector = dy.concatenate(
              [attend(input_mat, s, w1dt), last_output_embeddings]
          )
          s = s.add_input(vector)
          out_vector = w * s.output() + b
          probs = dy.softmax(out_vector).vec_value()
          next_char = probs.index(max(probs))
          last_output_embeddings = output_lookup[next_char]
          if int2char[next_char] == EOS:
              count_EOS += 1
              continue
  
          out += int2char[next_char]
      return out
  
file_path: dynet/examples/sequence-to-sequence/attention.py
imports: import dynet
  import random
label_desc: Use image node_id 6 for calling a global function with example usage: generate(in_seq, enc_fwd_lstm, enc_bwd_lstm, dec_lstm) and returns: out